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        <h1 class="title is-1 publication-title">
          Accelerating Neural Field Training via Soft Mining
        </h1>
        <div class="is-size-5 publication-authors">
          <span class="author-block">
            <a href="https://shakibakh.github.io/">Shakiba
              Kheradmand</a><sup>1</sup>,</span>
          <span class="author-block">
            <a href="http://drebain.com/"> Daniel Rebain</a><sup>1</sup>,</span>
          <span class="author-block">
            <a href="https://hippogriff.github.io/"> Gopal Sharma</a><sup>1</sup>,</span>
          <span class="author-block">
            <a href="http://www.hossamisack.com/">Hossam Isack</a><sup>2</sup>,
          </span>
          <span class="author-block">
            <a href="https://abhishekkar.info/">Abhishek Kar</a><sup>2</sup>
          </span>
          <br>
          <span class="author-block">
            <a href="https://taiya.github.io/">Andrea Tagliasacchi</a><sup>3, 4, 5</sup>
          </span>
          <span class="author-block">
            <a href="https://www.cs.ubc.ca/~kmyi/">Kwang Moo Yi</a><sup>1</sup>
          </span>
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        <div class="is-size-5 publication-authors">
          <span class="author-block"><sup>1</sup>University of British Columbia</span>
          <span class="author-block"><sup>2</sup>Google Research</span>
          <span class="author-block"><sup>3</sup>Google DeepMind</span> <br>
          <span class="author-block"><sup>4</sup>Simon Fraser University</span>
          <span class="author-block"><sup>5</sup>University of Toronto</span>
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                <span>arXiv</span>
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          <h2 class="title is-3">Abstract</h2>
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            <p>
              We present an approach to accelerate Neural Field training by efficiently selecting sampling locations.
              While Neural Fields have recently become popular, it is often trained by uniformly sampling the training
              domain, or through handcrafted heuristics.
              We show that improved convergence and final training quality can be achieved by a soft mining technique
              based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the
              corresponding loss by a scalar.
              To implement our idea we use Langevin Monte-Carlo sampling.
              We show that by doing so, regions with higher error are being selected more frequently, leading to more
              than 2x improvement in convergence speed.
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          <strong>Teaser –</strong> we introduce soft mining to accelerate neural field training.
          When applied to Neural Radiance Field (NeRF) training, our method significantly improves convergence.
          We visualize the error maps (blue denotes low error and red denotes high error) and the rendered novel views
          for uniform sampling and our method.
          We plot the convergence showing the Peak Signal-to-Noise Ratio (PSNR) for the corresponding scene.
          We render both images at 1k iterations of training, specified by the red dashed line in the (right) graph.
          Our method achieves the same PSNR significantly faster than the baselines.
        </h2>
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          <h2 class="title is-3">Results</h2>
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                        <source src="./static/videos/mic_good_video_3.mov" type="video/mp4">
                      </video>
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                      <video class="video" poster="" id="lego" autoplay controls muted loop playsinline width="2280">
                        <source src="./static/videos/lego_good_video_3.mov" type="video/mp4">
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                  <p style="margin-top: 10px;"><b>NeRF Example - Synthetic Dataset.</b> The evolution of test PSNR during
                    the training of our Soft Mining Sampling technique compared to
                    the Uniform Sampling baseline is illustrated using the 'Mic', 'Lego', and 'Chair' image from the
                    NeRF Synthetic dataset. The orange curve indicates the test PSNR of the uniform sampling
                    baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates
                    the current position on the curve.</p>
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                      <video class="video" poster="" id="trex" autoplay controls muted loop playsinline width="1280">
                        <source src="./static/videos/trex_good_video_3.mov" type="video/mp4">
                      </video>
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                      <video class="video" poster="" id="fortress" autoplay controls muted loop playsinline
                        width="1280">
                        <source src="./static/videos/fortress_good_video_3.mov" type="video/mp4">
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                      <video class="video" poster="" id="fern" autoplay controls muted loop playsinline width="1280">
                        <source src="./static/videos/fern_good_video_3.mov" type="video/mp4">
                      </video>
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                  </div>
                  <p style="margin-top: 10px;"><b>NeRF Example - LLFF Dataset.</b> The evolution of test PSNR during the
                    training of our Soft Mining Sampling technique compared to
                    the Uniform Sampling baseline is illustrated using the 'Trex, 'Fortress', and 'Fern' image from the
                    LLFF dataset. The orange curve indicates the test PSNR of the uniform sampling
                    baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates
                    the
                    current position on the curve.</p>
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                      <video class="video" poster="" id="pluto" autoplay controls muted loop playsinline width="1280">
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                      <video class="video" poster="" id="tokyo" autoplay controls muted loop playsinline width="1280">
                        <source src="./static/videos/all_tokyo_512_1.mov" type="video/mp4">
                      </video>
                    </div>
                  </div>
                  <p style="margin-top: 10px;"><b>2D Image Fitting Example.</b> The evolution of test PSNR during the
                    training of our Soft Mining Sampling technique compared to
                    the Uniform Sampling baseline is illustrated using the 'Pluto' and 'Tokyo' image for 2D image fitting
                    application with a batch size of 512.
                    The orange curve indicates the test PSNR of the uniform sampling
                    baseline, while the blue curve represents the test PSNR of our method. The yellow arrow indicates
                    the current position on the curve. The samples along with the error map is also provided.</p>
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  </section>


  <section class="section" id="BibTeX">
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      <h2 class="title">BibTeX</h2>
      <pre><code>@article{kheradsoftmining2023,
        author    = {Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi},
        title     = {Accelerating Neural Field Training via Soft Mining},
        journal   = {Arxiv},
        year      = {2023},
        }</code></pre>
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